### OURSACFastBase 0
CUDA_VISIBLE_DEVICES=0 nohup python main.py \
    --env_name AntEnv \
    --env_hook DominoHook \
    --use_weighted_info_nce \
    --method OURSACFastBase \
    --buffer_size 100000 \
    --encoder_eps_type all \
    --train_freq 16 \
    --gradient_steps 32 \
    --adversarial_loss_coef 20 \
    --learning_rate 5e-5 \
    --contrast_frame_stack 20 \
    --encoder_tau 0.05 \
    --seed 100 \
    --use_wandb \
    --test_envs "[([0.4, 0.5], [0.4, 0.5]),([0.40, 0.50], [1.50, 1.60]),([1.50, 1.60], [0.40, 0.50]),([1.50, 1.60], [1.50, 1.60])]" \
    --test_eps_num_per_env 10 \
    --train_envs "[([0.75,0.85], [0.75,0.85]),([0.75,0.85], [1.0,1.15,1.25]),([1.0,1.15,1.25], [0.75,0.85]),([1.0,1.15,1.25], [1.0,1.15,1.25])]" &

CUDA_VISIBLE_DEVICES=1 nohup python main.py \
    --env_name HalfCheetahEnv \
    --env_hook DominoHook \
    --use_weighted_info_nce \
    --method OURSACFastBase \
    --buffer_size 100000 \
    --encoder_eps_type all \
    --train_freq 16 \
    --gradient_steps 32 \
    --adversarial_loss_coef 20 \
    --learning_rate 5e-5 \
    --contrast_frame_stack 20 \
    --encoder_tau 0.05 \
    --seed 100 \
    --use_wandb \
    --test_envs "[([0.40, 0.50],[0.40, 0.50]),([0.40, 0.50],[1.50, 1.60]),([1.50, 1.60],[0.40, 0.50]),([1.50, 1.60],[1.50, 1.60])]" \
    --test_eps_num_per_env 10 \
    --train_envs "[([0.75,0.85], [0.75,0.85]),([0.75,0.85], [1.0,1.15,1.25]),([1.0,1.15,1.25], [0.75,0.85]),([1.0,1.15,1.25], [1.0,1.15,1.25])]" &

CUDA_VISIBLE_DEVICES=2 nohup python main.py \
    --env_name CrippleAntEnv \
    --env_hook DominoHook \
    --use_weighted_info_nce \
    --method OURSACFastBase \
    --buffer_size 100000 \
    --encoder_eps_type all \
    --train_freq 16 \
    --gradient_steps 32 \
    --adversarial_loss_coef 20 \
    --learning_rate 5e-5 \
    --contrast_frame_stack 20 \
    --encoder_tau 0.05 \
    --seed 100 \
    --test_envs "[([3], [0],[0.4, 0.5]),([3], [0],[1.5, 1.6])]" \
    --test_eps_num_per_env 10 \
    --use_wandb \
    --train_envs "[([0, 1], [0], [0.75,0.85]),([0, 1], [0], [0.75,0.85]),([2], [0], [0.75,0.85]),([2], [0], [1.0,1.15,1.25])]" &

CUDA_VISIBLE_DEVICES=0 nohup python main.py \
    --env_name CrippleHalfCheetahEnv \
    --env_hook DominoHook \
    --use_weighted_info_nce \
    --method OURSACFastBase \
    --buffer_size 100000 \
    --encoder_eps_type all \
    --train_freq 16 \
    --gradient_steps 32 \
    --adversarial_loss_coef 20 \
    --learning_rate 5e-5 \
    --contrast_frame_stack 20 \
    --encoder_tau 0.05 \
    --seed 100 \
    --use_wandb \
    --test_envs "[([4, 5], [0], [0.4, 0.5]),([4, 5], [0],[1.5, 1.6])]" \
    --test_eps_num_per_env 10 \
    --train_envs "[([0, 1], [0], [0.75,0.85]),([0, 1], [0], [1.0,1.15,1.25]),([2, 3], [0], [0.75,0.85]),([2, 3], [0], [1.0,1.15,1.25])]" &

CUDA_VISIBLE_DEVICES=1 nohup python main.py \
    --env_name SlimHumanoidEnv \
    --env_hook DominoHook \
    --use_weighted_info_nce \
    --method OURSACFastBase \
    --buffer_size 100000 \
    --encoder_eps_type all \
    --train_freq 16 \
    --gradient_steps 32 \
    --adversarial_loss_coef 20 \
    --learning_rate 5e-5 \
    --contrast_frame_stack 20 \
    --encoder_tau 0.05 \
    --use_wandb \
    --seed 100 \
    --test_envs "[([0.60, 0.70], [0.60, 0.70]),([0.60, 0.70], [1.50, 1.60]),([1.50, 1.60], [0.60, 0.70]),([1.50, 1.60], [1.50, 1.60])]" \
    --test_eps_num_per_env 10 \
    --train_envs "[([0.8, 0.9], [0.8, 0.9]),([0.8, 0.9], [1.0, 1.15, 1.25]),([1.0, 1.15, 1.25], [0.8, 0.9]),([1.0, 1.15, 1.25], [1.0, 1.15, 1.25])]" &

### OURSACFast one-0
CUDA_VISIBLE_DEVICES=2 nohup python main.py \
    --env_name AntEnv \
    --env_hook DominoHook \
    --use_weighted_info_nce \
    --method OURSACFastOne \
    --buffer_size 100000 \
    --encoder_eps_type all \
    --train_freq 16 \
    --gradient_steps 32 \
    --adversarial_loss_coef 20 \
    --learning_rate 5e-5 \
    --contrast_frame_stack 20 \
    --encoder_tau 0.05 \
    --seed 100 \
    --test_envs "[([0.4, 0.5], [0.4, 0.5]),([0.40, 0.50], [1.50, 1.60]),([1.50, 1.60], [0.40, 0.50]),([1.50, 1.60], [1.50, 1.60])]" \
    --test_eps_num_per_env 10 \
    --use_wandb \
    --train_envs "[([0.75,0.85], [0.75,0.85]),([0.75,0.85], [1.0,1.15,1.25]),([1.0,1.15,1.25], [0.75,0.85]),([1.0,1.15,1.25], [1.0,1.15,1.25])]" &

CUDA_VISIBLE_DEVICES=0 nohup python main.py \
    --env_name HalfCheetahEnv \
    --env_hook DominoHook \
    --use_weighted_info_nce \
    --method OURSACFastOne \
    --buffer_size 100000 \
    --encoder_eps_type all \
    --train_freq 16 \
    --gradient_steps 32 \
    --adversarial_loss_coef 20 \
    --learning_rate 5e-5 \
    --contrast_frame_stack 20 \
    --encoder_tau 0.05 \
    --seed 100 \
    --test_envs "[([0.40, 0.50],[0.40, 0.50]),([0.40, 0.50],[1.50, 1.60]),([1.50, 1.60],[0.40, 0.50]),([1.50, 1.60],[1.50, 1.60])]" \
    --test_eps_num_per_env 10 \
    --use_wandb \
    --train_envs "[([0.75,0.85], [0.75,0.85]),([0.75,0.85], [1.0,1.15,1.25]),([1.0,1.15,1.25], [0.75,0.85]),([1.0,1.15,1.25], [1.0,1.15,1.25])]" &

CUDA_VISIBLE_DEVICES=1 nohup python main.py \
    --env_name CrippleAntEnv \
    --env_hook DominoHook \
    --use_weighted_info_nce \
    --method OURSACFastOne \
    --buffer_size 100000 \
    --encoder_eps_type all \
    --train_freq 16 \
    --gradient_steps 32 \
    --adversarial_loss_coef 20 \
    --learning_rate 5e-5 \
    --contrast_frame_stack 20 \
    --encoder_tau 0.05 \
    --seed 100 \
    --test_envs "[([3], [0],[0.4, 0.5]),([3], [0],[1.5, 1.6])]" \
    --test_eps_num_per_env 10 \
    --use_wandb \
    --train_envs "[([0, 1], [0], [0.75,0.85]),([0, 1], [0], [0.75,0.85]),([2], [0], [0.75,0.85]),([2], [0], [1.0,1.15,1.25])]" &

CUDA_VISIBLE_DEVICES=2 nohup python main.py \
    --env_name CrippleHalfCheetahEnv \
    --env_hook DominoHook \
    --use_weighted_info_nce \
    --method OURSACFastOne \
    --buffer_size 100000 \
    --encoder_eps_type all \
    --train_freq 16 \
    --gradient_steps 32 \
    --adversarial_loss_coef 20 \
    --learning_rate 5e-5 \
    --contrast_frame_stack 20 \
    --encoder_tau 0.05 \
    --seed 100 \
    --test_envs "[([4, 5], [0], [0.4, 0.5]),([4, 5], [0],[1.5, 1.6])]" \
    --test_eps_num_per_env 10 \
    --use_wandb \
    --train_envs "[([0, 1], [0], [0.75,0.85]),([0, 1], [0], [1.0,1.15,1.25]),([2, 3], [0], [0.75,0.85]),([2, 3], [0], [1.0,1.15,1.25])]" &

CUDA_VISIBLE_DEVICES=0 nohup python main.py \
    --env_name SlimHumanoidEnv \
    --env_hook DominoHook \
    --use_weighted_info_nce \
    --method OURSACFastOne \
    --buffer_size 100000 \
    --encoder_eps_type all \
    --train_freq 16 \
    --gradient_steps 32 \
    --adversarial_loss_coef 20 \
    --learning_rate 5e-5 \
    --contrast_frame_stack 20 \
    --encoder_tau 0.05 \
    --seed 100 \
    --use_wandb \
    --test_envs "[([0.60, 0.70], [0.60, 0.70]),([0.60, 0.70], [1.50, 1.60]),([1.50, 1.60], [0.60, 0.70]),([1.50, 1.60], [1.50, 1.60])]" \
    --test_eps_num_per_env 10 \
    --train_envs "[([0.8, 0.9], [0.8, 0.9]),([0.8, 0.9], [1.0, 1.15, 1.25]),([1.0, 1.15, 1.25], [0.8, 0.9]),([1.0, 1.15, 1.25], [1.0, 1.15, 1.25])]" &

### OURSACFast all-0
CUDA_VISIBLE_DEVICES=1 nohup python main.py \
    --env_name AntEnv \
    --env_hook DominoHook \
    --adversarial_loss_coef 5 \
    --use_weighted_info_nce \
    --method OURSACFastAll \
    --buffer_size 100000 \
    --encoder_eps_type all \
    --train_freq 16 \
    --gradient_steps 32 \
    --adversarial_loss_coef 20 \
    --learning_rate 5e-5 \
    --contrast_frame_stack 20 \
    --encoder_tau 0.05 \
    --seed 100 \
    --use_wandb \
    --test_envs "[([0.4, 0.5], [0.4, 0.5]),([0.40, 0.50], [1.50, 1.60]),([1.50, 1.60], [0.40, 0.50]),([1.50, 1.60], [1.50, 1.60])]" \
    --test_eps_num_per_env 10 \
    --train_envs "[([0.75,0.85], [0.75,0.85]),([0.75,0.85], [1.0,1.15,1.25]),([1.0,1.15,1.25], [0.75,0.85]),([1.0,1.15,1.25], [1.0,1.15,1.25])]" &

CUDA_VISIBLE_DEVICES=2 nohup python main.py \
    --env_name HalfCheetahEnv \
    --env_hook DominoHook \
    --use_weighted_info_nce \
    --adversarial_loss_coef 5 \
    --method OURSACFastAll \
    --buffer_size 100000 \
    --encoder_eps_type all \
    --train_freq 16 \
    --gradient_steps 32 \
    --adversarial_loss_coef 20 \
    --learning_rate 5e-5 \
    --contrast_frame_stack 20 \
    --encoder_tau 0.05 \
    --use_wandb \
    --seed 100 \
    --test_envs "[([0.40, 0.50],[0.40, 0.50]),([0.40, 0.50],[1.50, 1.60]),([1.50, 1.60],[0.40, 0.50]),([1.50, 1.60],[1.50, 1.60])]" \
    --test_eps_num_per_env 10 \
    --train_envs "[([0.75,0.85], [0.75,0.85]),([0.75,0.85], [1.0,1.15,1.25]),([1.0,1.15,1.25], [0.75,0.85]),([1.0,1.15,1.25], [1.0,1.15,1.25])]" &

CUDA_VISIBLE_DEVICES=0 nohup python main.py \
    --env_name CrippleAntEnv \
    --env_hook DominoHook \
    --use_weighted_info_nce \
    --adversarial_loss_coef 5 \
    --method OURSACFastAll \
    --buffer_size 100000 \
    --encoder_eps_type all \
    --train_freq 16 \
    --gradient_steps 32 \
    --adversarial_loss_coef 20 \
    --learning_rate 5e-5 \
    --contrast_frame_stack 20 \
    --encoder_tau 0.05 \
    --seed 100 \
    --use_wandb \
    --test_envs "[([3], [0],[0.4, 0.5]),([3], [0],[1.5, 1.6])]" \
    --test_eps_num_per_env 10 \
    --train_envs "[([0, 1], [0], [0.75,0.85]),([0, 1], [0], [0.75,0.85]),([2], [0], [0.75,0.85]),([2], [0], [1.0,1.15,1.25])]" &

CUDA_VISIBLE_DEVICES=1 nohup python main.py \
    --env_name CrippleHalfCheetahEnv \
    --env_hook DominoHook \
    --use_weighted_info_nce \
    --method OURSACFastAll \
    --buffer_size 100000 \
    --adversarial_loss_coef 5 \
    --encoder_eps_type all \
    --train_freq 16 \
    --gradient_steps 32 \
    --adversarial_loss_coef 20 \
    --learning_rate 5e-5 \
    --contrast_frame_stack 20 \
    --encoder_tau 0.05 \
    --use_wandb \
    --seed 100 \
    --test_envs "[([4, 5], [0], [0.4, 0.5]),([4, 5], [0],[1.5, 1.6])]" \
    --test_eps_num_per_env 10 \
    --train_envs "[([0, 1], [0], [0.75,0.85]),([0, 1], [0], [1.0,1.15,1.25]),([2, 3], [0], [0.75,0.85]),([2, 3], [0], [1.0,1.15,1.25])]" &

CUDA_VISIBLE_DEVICES=2 nohup python main.py \
    --env_name SlimHumanoidEnv \
    --env_hook DominoHook \
    --use_weighted_info_nce \
    --adversarial_loss_coef 5 \
    --method OURSACFastAll \
    --buffer_size 100000 \
    --encoder_eps_type all \
    --train_freq 16 \
    --gradient_steps 32 \
    --adversarial_loss_coef 20 \
    --learning_rate 5e-5 \
    --contrast_frame_stack 20 \
    --encoder_tau 0.05 \
    --seed 100 \
    --use_wandb \
    --test_envs "[([0.60, 0.70], [0.60, 0.70]),([0.60, 0.70], [1.50, 1.60]),([1.50, 1.60], [0.60, 0.70]),([1.50, 1.60], [1.50, 1.60])]" \
    --test_eps_num_per_env 10 \
    --train_envs "[([0.8, 0.9], [0.8, 0.9]),([0.8, 0.9], [1.0, 1.15, 1.25]),([1.0, 1.15, 1.25], [0.8, 0.9]),([1.0, 1.15, 1.25], [1.0, 1.15, 1.25])]" &


CUDA_VISIBLE_DEVICES=0 nohup python main.py \
    --env_name HopperEnv \
    --env_hook DominoHook \
    --use_weighted_info_nce \
    --method OURSACFastAll \
    --buffer_size 100000 \
    --encoder_eps_type all \
    --train_freq 16 \
    --gradient_steps 32 \
    --adversarial_loss_coef 20 \
    --learning_rate 5e-5 \
    --contrast_frame_stack 20 \
    --encoder_tau 0.05 \
    --seed 102 \
    --test_envs "[([0.25, 0.375], [0.25, 0.375]),([0.25, 0.375], [1.75, 2.0]),([1.75, 2.0], [0.25, 0.375]),([1.75, 2.0], [1.75, 2.0])]" \
    --test_eps_num_per_env 10 \
    --use_wandb \
    --train_envs "[([0.5, 0.75, 1.0], [0.5, 0.75, 1.0]),([0.5, 0.75, 1.0], [1.25, 1.5]),([1.25, 1.5], [0.5, 0.75, 1.0]),([1.25, 1.5], [1.25, 1.5])]" &


CUDA_VISIBLE_DEVICES=1 nohup python main.py \
    --env_name HopperEnv \
    --env_hook DominoHook \
    --use_weighted_info_nce \
    --method OURSACFastOne \
    --buffer_size 100000 \
    --encoder_eps_type all \
    --train_freq 16 \
    --gradient_steps 32 \
    --adversarial_loss_coef 20 \
    --learning_rate 5e-5 \
    --contrast_frame_stack 20 \
    --encoder_tau 0.05 \
    --seed 102 \
    --test_envs "[([0.25, 0.375], [0.25, 0.375]),([0.25, 0.375], [1.75, 2.0]),([1.75, 2.0], [0.25, 0.375]),([1.75, 2.0], [1.75, 2.0])]" \
    --test_eps_num_per_env 10 \
    --use_wandb \
    --train_envs "[([0.5, 0.75, 1.0], [0.5, 0.75, 1.0]),([0.5, 0.75, 1.0], [1.25, 1.5]),([1.25, 1.5], [0.5, 0.75, 1.0]),([1.25, 1.5], [1.25, 1.5])]" &


CUDA_VISIBLE_DEVICES=2 nohup python main.py \
    --env_name HopperEnv \
    --env_hook DominoHook \
    --use_weighted_info_nce \
    --method OURSACFastBase \
    --buffer_size 100000 \
    --encoder_eps_type all \
    --train_freq 16 \
    --gradient_steps 32 \
    --adversarial_loss_coef 20 \
    --learning_rate 5e-5 \
    --contrast_frame_stack 20 \
    --encoder_tau 0.05 \
    --seed 102 \
    --test_envs "[([0.25, 0.375], [0.25, 0.375]),([0.25, 0.375], [1.75, 2.0]),([1.75, 2.0], [0.25, 0.375]),([1.75, 2.0], [1.75, 2.0])]" \
    --test_eps_num_per_env 10 \
    --use_wandb \
    --train_envs "[([0.5, 0.75, 1.0], [0.5, 0.75, 1.0]),([0.5, 0.75, 1.0], [1.25, 1.5]),([1.25, 1.5], [0.5, 0.75, 1.0]),([1.25, 1.5], [1.25, 1.5])]" &



### OURSACFastBase 0
CUDA_VISIBLE_DEVICES=0 nohup python main.py \
    --env_name AntEnv \
    --env_hook DominoHook \
    --use_weighted_info_nce \
    --method RNNSAC \
    --buffer_size 100000 \
    --encoder_eps_type all \
    --train_freq 16 \
    --gradient_steps 32 \
    --adversarial_loss_coef 20 \
    --learning_rate 5e-5 \
    --contrast_frame_stack 20 \
    --encoder_tau 0.05 \
    --seed 100 \
    --use_wandb \
    --test_envs "[([0.4, 0.5], [0.4, 0.5]),([0.40, 0.50], [1.50, 1.60]),([1.50, 1.60], [0.40, 0.50]),([1.50, 1.60], [1.50, 1.60])]" \
    --test_eps_num_per_env 10 \
    --train_envs "[([0.75,0.85], [0.75,0.85]),([0.75,0.85], [1.0,1.15,1.25]),([1.0,1.15,1.25], [0.75,0.85]),([1.0,1.15,1.25], [1.0,1.15,1.25])]" &

CUDA_VISIBLE_DEVICES=1 nohup python main.py \
    --env_name HalfCheetahEnv \
    --env_hook DominoHook \
    --use_weighted_info_nce \
    --method RNNSAC \
    --buffer_size 100000 \
    --encoder_eps_type all \
    --train_freq 16 \
    --gradient_steps 32 \
    --adversarial_loss_coef 20 \
    --learning_rate 5e-5 \
    --contrast_frame_stack 20 \
    --encoder_tau 0.05 \
    --seed 100 \
    --use_wandb \
    --test_envs "[([0.40, 0.50],[0.40, 0.50]),([0.40, 0.50],[1.50, 1.60]),([1.50, 1.60],[0.40, 0.50]),([1.50, 1.60],[1.50, 1.60])]" \
    --test_eps_num_per_env 10 \
    --train_envs "[([0.75,0.85], [0.75,0.85]),([0.75,0.85], [1.0,1.15,1.25]),([1.0,1.15,1.25], [0.75,0.85]),([1.0,1.15,1.25], [1.0,1.15,1.25])]" &

CUDA_VISIBLE_DEVICES=2 nohup python main.py \
    --env_name CrippleAntEnv \
    --env_hook DominoHook \
    --use_weighted_info_nce \
    --method RNNSAC \
    --buffer_size 100000 \
    --encoder_eps_type all \
    --train_freq 16 \
    --gradient_steps 32 \
    --adversarial_loss_coef 20 \
    --learning_rate 5e-5 \
    --contrast_frame_stack 20 \
    --encoder_tau 0.05 \
    --seed 100 \
    --test_envs "[([3], [0],[0.4, 0.5]),([3], [0],[1.5, 1.6])]" \
    --test_eps_num_per_env 10 \
    --use_wandb \
    --train_envs "[([0, 1], [0], [0.75,0.85]),([0, 1], [0], [0.75,0.85]),([2], [0], [0.75,0.85]),([2], [0], [1.0,1.15,1.25])]" &

CUDA_VISIBLE_DEVICES=0 nohup python main.py \
    --env_name CrippleHalfCheetahEnv \
    --env_hook DominoHook \
    --use_weighted_info_nce \
    --method RNNSAC \
    --buffer_size 100000 \
    --encoder_eps_type all \
    --train_freq 16 \
    --gradient_steps 32 \
    --adversarial_loss_coef 20 \
    --learning_rate 5e-5 \
    --contrast_frame_stack 20 \
    --encoder_tau 0.05 \
    --seed 100 \
    --use_wandb \
    --test_envs "[([4, 5], [0], [0.4, 0.5]),([4, 5], [0],[1.5, 1.6])]" \
    --test_eps_num_per_env 10 \
    --train_envs "[([0, 1], [0], [0.75,0.85]),([0, 1], [0], [1.0,1.15,1.25]),([2, 3], [0], [0.75,0.85]),([2, 3], [0], [1.0,1.15,1.25])]" &

CUDA_VISIBLE_DEVICES=1 nohup python main.py \
    --env_name SlimHumanoidEnv \
    --env_hook DominoHook \
    --use_weighted_info_nce \
    --method RNNSAC \
    --buffer_size 100000 \
    --encoder_eps_type all \
    --train_freq 16 \
    --gradient_steps 32 \
    --adversarial_loss_coef 20 \
    --learning_rate 5e-5 \
    --contrast_frame_stack 20 \
    --encoder_tau 0.05 \
    --use_wandb \
    --seed 100 \
    --test_envs "[([0.60, 0.70], [0.60, 0.70]),([0.60, 0.70], [1.50, 1.60]),([1.50, 1.60], [0.60, 0.70]),([1.50, 1.60], [1.50, 1.60])]" \
    --test_eps_num_per_env 10 \
    --train_envs "[([0.8, 0.9], [0.8, 0.9]),([0.8, 0.9], [1.0, 1.15, 1.25]),([1.0, 1.15, 1.25], [0.8, 0.9]),([1.0, 1.15, 1.25], [1.0, 1.15, 1.25])]" &

CUDA_VISIBLE_DEVICES=2 nohup python main.py \
    --env_name HopperEnv \
    --env_hook DominoHook \
    --use_weighted_info_nce \
    --method RNNSAC \
    --buffer_size 100000 \
    --encoder_eps_type all \
    --train_freq 16 \
    --gradient_steps 32 \
    --adversarial_loss_coef 20 \
    --learning_rate 5e-5 \
    --contrast_frame_stack 20 \
    --encoder_tau 0.05 \
    --seed 102 \
    --test_envs "[([0.25, 0.375], [0.25, 0.375]),([0.25, 0.375], [1.75, 2.0]),([1.75, 2.0], [0.25, 0.375]),([1.75, 2.0], [1.75, 2.0])]" \
    --test_eps_num_per_env 10 \
    --use_wandb \
    --train_envs "[([0.5, 0.75, 1.0], [0.5, 0.75, 1.0]),([0.5, 0.75, 1.0], [1.25, 1.5]),([1.25, 1.5], [0.5, 0.75, 1.0]),([1.25, 1.5], [1.25, 1.5])]" &